{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T14:20:24Z","timestamp":1776435624198,"version":"3.51.2"},"reference-count":76,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SILVADAPT.NET","award":["RED2018-102719-T"],"award-info":[{"award-number":["RED2018-102719-T"]}]},{"name":"SILVADAPT.NET","award":["2822\/2021"],"award-info":[{"award-number":["2822\/2021"]}]},{"name":"SILVADAPT.NET","award":["PID2021-128463OB-I00"],"award-info":[{"award-number":["PID2021-128463OB-I00"]}]},{"name":"EVIDENCE","award":["RED2018-102719-T"],"award-info":[{"award-number":["RED2018-102719-T"]}]},{"name":"EVIDENCE","award":["2822\/2021"],"award-info":[{"award-number":["2822\/2021"]}]},{"name":"EVIDENCE","award":["PID2021-128463OB-I00"],"award-info":[{"award-number":["PID2021-128463OB-I00"]}]},{"name":"REMEDIO","award":["RED2018-102719-T"],"award-info":[{"award-number":["RED2018-102719-T"]}]},{"name":"REMEDIO","award":["2822\/2021"],"award-info":[{"award-number":["2822\/2021"]}]},{"name":"REMEDIO","award":["PID2021-128463OB-I00"],"award-info":[{"award-number":["PID2021-128463OB-I00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Currently, climate change requires the quantification of carbon stored in forest biomass. Synthetic aperture radar (SAR) data offers a significant advantage over other remote detection measurement methods in providing structural and biomass-related information about ecosystems. This study aimed to develop non-parametric Random Forest regression models to assess the changes in the aboveground forest biomass (AGB), basal area (G), and tree density (N) of Mediterranean pine forests by integrating ALOS-PALSAR, Sentinel 1, and Landsat 8 data. Variables selected from the Random Forest models were related to NDVI and optical textural variables. For 2015, the biomass models with the highest performance integrated ALS-ALOS2-Sentinel 1-Landsat 8 data (R2 = 0.59) by following the model using ALS data (R2 = 0.56), and ALOS2-Sentinel 1-Landsat 8 (R2 = 0.50). The validation set showed that R2 values vary from 0.55 (ALOS2-Sentinel 1-Landsat 8) to 0.60 (ALS-ALOS2-Sentinel 1-Landsat 8 model) with RMSE below 20 Mg ha\u22121. It is noteworthy that the individual Sentinel 1 (R2 = 0.49). and Landsat 8 (R2 = 0.47) models yielded equivalent results. For 2020, the AGB model ALOS2-Sentinel 1-Landsat 8 had a performance of R2 = 0.55 (validation R2 = 0.70) and a RMSE of 9.93 Mg ha\u22121. For the 2015 forest structural variables, Random Forest models, including ALOS PAL-SAR 2-Sentinel 1 Landsat 8 explained between 30% and 55% of the total variance, and for the 2020 models, they explained between 25% and 55%. Maps of the forests\u2019 structural variables were generated for 2015 and 2020 to assess the changes during this period using the ALOS PALSAR 2-Sentinel 1-Landsat 8 model. Aboveground biomass (AGB), diameter at breast height (dbh), and dominant height (Ho) maps were consistent throughout the entire study area. However, the Random Forest models underestimated higher biomass levels (&gt;100 Mg ha\u22121) and overestimated moderate biomass levels (30\u201345 Mg ha\u22121). The AGB change map showed values ranging from gains of 43.3 Mg ha\u22121 to losses of \u221268.8 Mg ha\u22121 during the study period. The integration of open-access satellite optical and SAR data can significantly enhance AGB estimates to achieve consistent and long-term monitoring of forest carbon dynamics.<\/jats:p>","DOI":"10.3390\/rs15133430","type":"journal-article","created":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T02:28:46Z","timestamp":1688696926000},"page":"3430","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Temporal Changes in Mediterranean Pine Forest Biomass Using Synergy Models of ALOS PALSAR-Sentinel 1-Landsat 8 Sensors"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3371-6147","authenticated-orcid":false,"given":"Edward A.","family":"Velasco Pereira","sequence":"first","affiliation":[{"name":"Silviculture Laboratory, Dendrochronology, and Climate Change, DendrodatLab-ERSAF, Department of Forestry Engineering, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, E-14071 C\u00f3rdoba, Spain"}]},{"given":"Mar\u00eda A.","family":"Varo Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Silviculture Laboratory, Dendrochronology, and Climate Change, DendrodatLab-ERSAF, Department of Forestry Engineering, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, E-14071 C\u00f3rdoba, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1999-3415","authenticated-orcid":false,"given":"Francisco J.","family":"Ruiz G\u00f3mez","sequence":"additional","affiliation":[{"name":"Silviculture Laboratory, Dendrochronology, and Climate Change, DendrodatLab-ERSAF, Department of Forestry Engineering, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, E-14071 C\u00f3rdoba, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3470-8640","authenticated-orcid":false,"given":"Rafael M.","family":"Navarro-Cerrillo","sequence":"additional","affiliation":[{"name":"Silviculture Laboratory, Dendrochronology, and Climate Change, DendrodatLab-ERSAF, Department of Forestry Engineering, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, E-14071 C\u00f3rdoba, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Novo-Fern\u00e1ndez, A., Barrio-Anta, M., Recondo, C., C\u00e1mara-Obreg\u00f3n, A., and L\u00f3pez-S\u00e1nchez, C.A. (2019). Integration of national forest inventory and nationwide airborne laser scanning data to improve forest yield predictions in north-western Spain. 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